The generative adversarial network works well on images with low resolutions
(\(128 \times 128\) ) and can even compete with human performance,
but it mainly struggles on images with high-resolution and suffers from
instability problem] during training.
During the last few years, several architectures [1]}, [2]}, [3]}
have been proposed to overcome these limitations and solve various tasks.
